Paper Citation Record · LEDGER

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2103.11251.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2103.11251 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:25:10.325887Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

22
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d9c241d5-bf66-4cf2-af1d-760d741f0e97 · inbound

Improving Dictionary Learning with Gated Sparse Autoencoders cites this paper.

Improving Dictionary Learning with Gated Sparse Autoencoders Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 195

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T19:42:32.074369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ec1e0824-146f-443b-ba27-226c42623657 · inbound

Explaining Model Overfitting in CNNs via GMM Clustering cites this paper.

Explaining Model Overfitting in CNNs via GMM Clustering Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:10.325887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2f8a845-fb1f-4b97-ba33-cc4a3c63a697 · inbound

Representation learning for fast radio burst dynamic spectra cites this paper.

Representation learning for fast radio burst dynamic spectra Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T14:10:53.395776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e21e9ec9-d4a7-46c2-9aac-f4e737005bea · inbound

SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks cites this paper.

SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T13:32:45.225767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3518b2f6-0f4c-4a9c-b531-a96107e6cc1a · inbound

Parallel Key-Value Cache Fusion for Position Invariant RAG cites this paper.

Parallel Key-Value Cache Fusion for Position Invariant RAG Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T20:44:49.126127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80028ca9-a2fa-4de5-90a3-2842d547fbf9 · inbound

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning cites this paper.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 112

Resolution: unresolved; no resolver link, observed 2026-08-09T17:58:35.824902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b49314eb-b9eb-4420-96ca-a0ab001abe5a · inbound

On the definition and importance of interpretability in scientific machine learning cites this paper.

On the definition and importance of interpretability in scientific machine learning Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:11:38.611213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 833c3f63-3e79-41f5-91e8-d66e8a42f87a · inbound

Augmented Vision-Language Models: A Systematic Review cites this paper.

Augmented Vision-Language Models: A Systematic Review Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T14:33:40.159564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9abacdf4-60a8-4b41-85dd-45ffce99e7bb · inbound

Attention of a Kiss: Exploring Attention Maps in Video Diffusion for XAIxArts cites this paper.

Attention of a Kiss: Exploring Attention Maps in Video Diffusion for XAIxArts Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T13:27:41.904008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21976a3c-2e90-4511-b30c-e7d5b445cd3c · inbound

Agentic-imodels: Evolving agentic interpretability tools via autoresearch cites this paper.

Agentic-imodels: Evolving agentic interpretability tools via autoresearch Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:36:36.277880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 332a184d-137f-4399-bd06-4bd0d8db3fbe · inbound

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces cites this paper.

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 172

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:17:54.285809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a2217ec4-c843-48bd-a0b1-813eadef7fe6 · inbound

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language cites this paper.

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 114

Resolution: unresolved; no resolver link, observed 2026-07-11T23:16:58.545731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5af1c2e4-6860-46f5-bd42-5d7e47b07006 · inbound

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language cites this paper.

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Reference 114

Resolution: unresolved; no resolver link, observed 2026-07-13T07:02:13.140334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.